Abstract
BackgroundHomology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. A major limitation of these methods is the absence of well-characterized sequences for certain functions. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. In the current work, we employ both homology and context-based methods, incrementally, to identify local holes and chokepoints, whose presence in the Mycobacterium tuberculosis genome is indicated based on its interaction with known proteins in a metabolic network context, but have not been annotated. We have developed two computational procedures using network theory to identify orphan enzymes (‘Hole finding protocol’) coupled with the identification of candidate proteins for the predicted orphan enzyme (‘Hole filling protocol’). We propose an integrated interaction score based on scores from the STRING database to identify candidate protein sequences for the orphan enzymes from M. tuberculosis, as a case study, which are most likely to perform the missing function.ResultsThe application of an automated homology-based enzyme identification protocol, ModEnzA, on M. tuberculosis genome yielded 56 novel enzyme predictions. We further predicted 74 putative local holes, 6 choke points, and 3 high confidence local holes in the genome using ‘Hole finding protocol’. The ‘Hole-filling protocol’ was validated on the E. coli genome using artificial in-silico enzyme knockouts where our method showed 25% increased accuracy, compared to other methods, in assigning the correct sequence for the knocked-out enzyme amongst the top 10 ranks. The method was further validated on 8 additional genomes.ConclusionsWe have developed methods that can be generalized to augment homology-based annotation to identify missing enzyme coding genes and to predict a candidate protein for them. For pathogens such as M. tuberculosis, this work holds significance in terms of increasing the protein repertoire and thereby, the potential for identifying novel drug targets.
Highlights
Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data
As a high confidence filter, we only considered a node as a potential hole/missing enzyme when all the immediate neighbors for that node were present in M. tb
There were a total of 459 metabolic proteins and 3375 non-metabolic proteins in E. coli (the actual number of metabolic and non-metabolic proteins is higher; in this study, we have only reported the number of proteins which had mappings to ENZYME CLASS (EC) numbers and STRING internal identifiers so that we can retrieve the combined score of association for the neighbors extracted from the EE graph, please refer to Additional file 1: Supplementary Fig. 1 for details about mapping the different IDs)
Summary
Homology based methods are one of the most important and widely used approaches for functional annotation of high-throughput microbial genome data. The non-homology methods based on the context and the interactions of a protein are very useful for identifying missing metabolic activities and functional annotation in the absence of significant sequence similarity. There are limitations of this method such as the presence of low complexity regions which can give artificially high scores, significant variation within a protein family, a small number of substrate-specificity determining residues, and discontinuous conserved patterns. To improve these limitations the development of better sensitive methods using sequenceprofile comparisons was implemented
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